Print Email Facebook Twitter 28 nm CMOS Array of Strips for Pedestrian Detection Title 28 nm CMOS Array of Strips for Pedestrian Detection Author van den Bogert, Bart (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Microelectronics) Contributor Neto, Andrea (mentor) Llombart Juan, Nuria (mentor) van Berkel, Sven (mentor) Degree granting institution Delft University of Technology Date 2018-07-06 Abstract Due to technology trends such as autonomous driving, the need for robust safety in the automotive industry increases. The safety comes from sensors that are able to image the environment and systems that use this information to avoid incidents and retain maximum safety. Detection of pedestrian has high importance since a missed detection can be lethal. Current solutions for pedestrian detection are based on infrared and optical cameras. With these solutions it can be challenging to detect pedestrians under conditions such as cold/foggy conditions, especially during nighttime. In this scenario through clothes penetration of infrared radiation is poor. Instead, terahertz radiation penetrates better through clothes, making terahertz imagers a viable solution to increase safety in theframework of autonomous driving.The focus on automotive sensors requires that the solution needs to be low-cost, low power and compact. Traditional passive terahertz detectors are based on cryogenically cooling or active illumination of the target to maximize the sensitivity, since this is not applicable in automotive designs we design an array that can be combined with direct detectors to increases the sensitivity by maximizing the effective bandwidth. The sensor needs to have sufficient resolution to detect the pedestrians at distances up to 10 meters combined with sufficient sensitivity to also perform in weather conditions such as fog. Subject PedestriansDetectionTerahertzArray of elementsArray of StripsSpectral Domain MethodsCSTSensing To reference this document use: http://resolver.tudelft.nl/uuid:31adea0f-92ae-4411-ab3f-c1045baec523 Embargo date 2018-09-03 Part of collection Student theses Document type master thesis Rights © 2018 Bart van den Bogert Files PDF BvandenBogert_MSc_Thesis.pdf 24.63 MB Close viewer /islandora/object/uuid:31adea0f-92ae-4411-ab3f-c1045baec523/datastream/OBJ/view